Infectious disease is the result of actions and counter-actions performed by an infecting pathogen, the immune system, or both. Because such actions occur over time, and both the microbe and the immune system may differ in many aspects, different outcomes may occur over time, i.e., immuno-microbial interactions are not static or constant.
The present invention addresses several problems and needs described in the medical literature—some of these problems and needs known for more than 30 years. One such problem is the ‘cost of dichotomization.’ The cost of dichotomization is an error-prone method. (Cohen J., THE COST OF DICHOTOMIZATION. Applied Psychological Measurement 7: 249; 1983). When a continuous variable (such as the relative percentage of a cell type) is divided into two subsets (dichotomized) and the data subsets above or below a cut-off value receive non-continuous (discrete) labels (such as ‘yes/no’, ‘negative’/′positive′, or ‘non-inflamed’/′inflamed′), a substantial number of (false-negative, false-positive) errors are generated. For example, numerous true ‘negative’ observations will be found within the data range of ‘positives’ (false positives) and vice versa—numerous true ‘positive’ data points will be located within the data range of ‘negatives’ (false negatives).
The cost of dichotomization problem is shown in FIGS. 1A-D. The relative percentage of three cell types (lymphocytes [L], polymorphonuclear cell or neutrophil [PMN or N], and macrophage/monocyte [M]) failed to distinguish non-infected from infected individuals. Instead, overlapping leukocyte values were observed (rectangles, FIG. 1A). This means that any cut-off value of any of the cell types considered would result in false-negatives and/or false-positives. The ‘cost’ associated with ‘dichotomization’ is also revealed by linear models: if data points from infected and non-infected individuals were assumed to be linearly distributed, the point where the two data curves cross one another could be determined (vertical line, FIG. 1B). The cut-off point determines the data range within which data points of one category predominate over observations of the other category. Indeed, most ‘infected’ individuals predominated above such point (at the right half of the plot) while ‘non-infected’ individuals predominated below such point (left half of the plot). However, because such curves do not end at such point, a large error area can be observed: out of 24 data points, at least 5 false negative (FN) and 5 false positive (FP) data points are observed (due to similar values, 4 data points are not observed—they overlap, FIG. 1C). Such errors represent a 50% (10/20) total error rate (FIG. 1C). The high cost of the dichotomization procedure (the classic or current paradigm) is not the result of a particular cell type. If, instead of PMN %, lymphocyte percentages were considered, two-digit percent errors would also be observed (FIG. 1D). Two-digit percent errors are frequently found in the prior art when a cut-off value is used. ‘Dichotomization’ is also costly because, in tabular or any non three-dimensional (3-D) data format, data points perpendicular to the plane of analysis are missed.
Another problem in the prior art is delayed information, where information is not available in real time. One other problem in the prior art involves information loss, such as non-interpretable data. Such data is unusable data because it lacks differentiation of different data classes.